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02/18/2023
Approximate Thompson Sampling via Epistemic Neural Networks
Thompson sampling (TS) is a popular heuristic for action selection, but ...
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07/01/2022
Robustness of Epinets against Distributional Shifts
Recent work introduced the epinet as a new approach to uncertainty model...
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06/08/2022
Ensembles for Uncertainty Estimation: Benefits of Prior Functions and Bootstrapping
In machine learning, an agent needs to estimate uncertainty to efficient...
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03/02/2022
An Analysis of Ensemble Sampling
Ensemble sampling serves as a practical approximation to Thompson sampli...
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02/28/2022
Evaluating High-Order Predictive Distributions in Deep Learning
Most work on supervised learning research has focused on marginal predic...
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11/05/2021
Event-based Motion Segmentation by Cascaded Two-Level Multi-Model Fitting
Among prerequisites for a synthetic agent to interact with dynamic scene...
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10/09/2021
Evaluating Predictive Distributions: Does Bayesian Deep Learning Work?
Posterior predictive distributions quantify uncertainties ignored by poi...
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07/20/2021
Evaluating Probabilistic Inference in Deep Learning: Beyond Marginal Predictions
A fundamental challenge for any intelligent system is prediction: given ...
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03/14/2021
GVINS: Tightly Coupled GNSS-Visual-Inertial for Smooth and Consistent State Estimation
Visual-Inertial odometry is known to suffer from drifting especially ove...
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03/06/2021
Reinforcement Learning, Bit by Bit
Reinforcement learning agents have demonstrated remarkable achievements ...
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12/16/2020
Event-based Motion Segmentation with Spatio-Temporal Graph Cuts
Identifying independently moving objects is an essential task for dynami...
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06/12/2020
Hypermodels for Exploration
We study the use of hypermodels to represent epistemic uncertainty and g...
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11/21/2019
Information-Theoretic Confidence Bounds for Reinforcement Learning
We integrate information-theoretic concepts into the design and analysis...
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05/20/2017